KNN using brute force and ball trees implemented in Python/Cython
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Updated
Mar 26, 2019 - Python
KNN using brute force and ball trees implemented in Python/Cython
Practice Material
🌼 Classify the different species of the Iris flower.
🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset
Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai
PyTorch implementations of the beta divergence loss.
Classification of IRIS Dataset using various distance metrics.
Similarity and distance measures for clustering and record linkage applications in R
Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points
Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.
DTW(Dynamic Time Warping) & Subsequence-DTW Python Module
This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.
The Python 3 library for Multi-Criteria Decision Analysis.
Classification model to categorize clothing items into distinct classes
Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.
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